In this paper, we present an unsupervised single-channel method for joint
blind dereverberation and room impulse response estimation, based on posterior
sampling with diffusion models. We parameterize the reverberation operator
using a filter with exponential decay for each frequency subband, and
iteratively estimate the corresponding parameters as the speech utterance gets
refined along the reverse diffusion trajectory. A measurement consistency
criterion enforces the fidelity of the generated speech with the reverberant
measurement, while an unconditional diffusion model implements a strong prior
for clean speech generation. Without any knowledge of the room impulse response
nor any coupled reverberant-anechoic data, we can successfully perform
dereverberation in various acoustic scenarios. Our method significantly
outperforms previous blind unsupervised baselines, and we demonstrate its
increased robustness to unseen acoustic conditions in comparison to blind
supervised methods. Audio samples and code are available online.

该论文提出了一种基于扩散模型的无监督单声道方法，用于联合盲去混响和房间脉冲响应估计。在各个频率子带上使用带指数衰减的滤波器对混响运算符进行参数化，并沿反向扩散轨迹迭代估计相应的参数。通过测量一致性准则强化生成语音与混响测量的真实性，同时无条件的扩散模型实现了对清晰语音生成的强先验。在没有任何关于房间脉冲响应或混响 - 无混响耦合数据的信息下，我们能够在各种声学场景中成功地进行去混响。我们的方法明显优于先前的盲无监督基准线，并且我们展示了相对于盲监督方法对未见声学条件更具鲁棒性。在线上可获取音频样本和代码。

BUDDy: 单通道盲无监督除混响实现弥散模型

BUDDy: Single-Channel Blind Unsupervised Dereverberation with Diffusion  Models

The availability of realistic simulated corpora is of key importance for the
future progress of distant speech recognition technology. The reliability,
flexibility and low computational cost of a data simulation process may
ultimately allow researchers to train, tune and test different techniques in a
variety of acoustic scenarios, avoiding the laborious effort of directly
recording real data from the targeted environment.
In the last decade, several simulated corpora have been released to the
research community, including the data-sets distributed in the context of
projects and international challenges, such as CHiME and REVERB. These efforts
were extremely useful to derive baselines and common evaluation frameworks for
comparison purposes. At the same time, in many cases they highlighted the need
of a better coherence between real and simulated conditions.
In this paper, we examine this issue and we describe our approach to the
generation of realistic corpora in a domestic context. Experimental validation,
conducted in a multi-microphone scenario, shows that a comparable performance
trend can be observed with both real and simulated data across different
recognition frameworks, acoustic models, as well as multi-microphone processing
techniques.

本文重点讨论了模拟语音数据集的重要性以及如何利用数据模拟技术来生成与现实环境密切相关的语音数据集，并通过多麦克风的实验验证了其在各种情况下的可行性。